Full-spike deep mutational scanning helps predict the evolutionary success of SARS-CoV-2 clades

Abstract

SARS-CoV-2 variants acquire mutations in spike that promote immune evasion and impact other properties that contribute to viral fitness such as ACE2 receptor binding and cell entry. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning to measure how >9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry, or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully impacted ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456, and 473-however, the antigenic impacts of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.

Fig. 1:. Deep mutational scanning to measure phenotypes of the XBB.1.5 and BA.2 spikes

a, We measure the effects of mutations in spike on cell entry, receptor binding and serum escape. We then use these measurements to predict the evolutionary success of human SARS-CoV-2 clades. b, Distribution of effects of mutations in XBB.1.5 and BA.2 spikes on entry into 293T-ACE2 cells for all mutations in the deep mutational scanning libraries, stratified by the type of mutation and the domain in spike. Negative values indicate worse cell entry than the unmutated parental spike. Note that the library design favored introduction of substitutions and deletions that are well tolerated by spike, explaining why many mutations of these types have neutral to only modestly deleterious impacts on cell entry. c, Cell entry effects of mutations F456L, P1143L and deletion of V483 relative to the distribution of effects of all substitution and deletion mutations in the libraries. Interactive heatmaps with effects of individual mutations across the whole spike on cell entry are at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/293T_high_ACE2_entry_func_effects.html and https://dms-vep.github.io/SARS-CoV-2_Omicron_BA.2_spike_ACE2_affinity/htmls/293T_high_ACE2_entry_func_effects.html. The boxes in panels b and c span the interquartile range, with the horizontal white line indicating the median. For panel c, the effect of deleting V483 was not measured in the BA.2 spike.

Fig. 2:. Effects of mutations on full-spike ACE2 binding measured using pseudovirus deep mutational scanning

a, Neutralization of pseudoviruses with the indicated spikes by soluble monomeric ACE2. Viruses with spikes that have stronger binding toACE2 are neutralized more efficiently by soluble ACE2 (lower NT50), whereas viruses with spikes with worse binding are neutralized more weakly. ACE2 affinity values measured by surface plasmon resonance for BA.2 and Wu-1+D614G are shown in brackets. b, Correlation between neutralization NT50 by soluble ACE2 versus the RBD affinity for ACE2 as measured by titrations using yeast-displayed RBD. c, Effects of NTD and RBD mutations on full-spike ACE2 binding as measured using pseudovirus deep mutational scanning. Mutations that enhance ACE2 binding are shaded blue, mutations that decrease affinity are shaded orange, mutations that are too deleterious for cell entry to be measured in the binding assay are dark gray, and light gray indicates mutations not present in our libraries. Interactive heatmaps showing mutational effects on ACE2 binding for the full XBB.1.5 and BA.2 spikes are at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/monomeric_ACE2_mut_effect.html and https://dms-vep.github.io/SARS-CoV-2_Omicron_BA.2_spike_ACE2_affinity/htmls/monomeric_ACE2_mut_effect.html. Note that a few sites are missing in the static heatmap in this figure due to lack of coverage or deletions in the XBB.1.5 spike; see the interactive heatmaps for per-site numbering. d, Correlations between the effects of RBD mutations on ACE2 binding measured using the pseudovirus-based approach (this study) and yeast-based RBD display^,. e, Distribution of effects of individual mutations on full-spike ACE2 binding for all functionally tolerated mutations in our libraries, stratified by RBD versus non-RBD mutations. Note that effects of magnitude greater than two are clamped to the limits of the plots’ x-axes.

Fig. 3:. Non-RBD mutations impact ACE2 binding

a, ACE2 binding measurements using mass photometry. Histogram on the left shows distribution of spike molecular mass when no (S^0xACE2) one (S^1xACE2) or two (S^2xACE2) ACE2 molecules are bound. We measure how this mass distribution changes as spike is incubated with increasing concentrations of soluble dimeric ACE2. RBD occupancy is the fraction of RBDs bound to ACE2, calculated using Gaussian components for S^0xACE, S^1xACE2 and S^2xACE2 at each ACE2 concentration. b, RBD occupancy measured using mass photometry for different BA.2 spike variants. Left panel shows that a BA.2 spike mutation known to increase ACE2 binding (R493Q/blue) has greater RBD occupancy relative to unmutated BA.2 (black) spike, while a mutation known to decrease ACE2 binding (R498V/green) has lower RBD occupancy. Panels on the right show RBD occupancy for BA.2 spike variants with mutations in S^1xACE2 or S^2xACE2 subunits measured to increase ACE2 binding in the deep mutational scanning. c, Non-RBD mutations measured to increase ACE2 binding in deep mutational scanning experiments that are observed to have arisen independently as defining mutations in at least three XBB-descended clades.

Fig. 4:. Serum antibody escape mutations for individuals with prior XBB* infections

a, Escape at each site in the XBB.1.5 spike averaged across 10 sera collected from individuals with prior XBB* infections. The points indicate the total positive escape caused by all mutations at each site. See https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/summary_overlaid.html for an interactive version of this plot with additional mutation-level data. b, Zoomed view of the escape at each site in RBD with each line representing one of the 10 sera. Key sites are labeled with red circles indicating escape for each of the 10 sera. c, Logo plots showing the 16 sites of greatest total escape after averaging across the sera. Letter heights indicate escape caused by mutation to that amino acid, and letters are colored light yellow to dark brown depending on the impact of that mutation on ACE2 binding (cf. color key). The top plot shows all amino-acid mutations measured, and the bottom plot shows just amino acids accessible by a single nucleotide mutation to the XBB.1.5 spike. d, Left: correlation between DMS escape scores and pseudovirus neutralization assay IC90 values for three sera. Right: logo plot showing escape for all sites with mutations validated in the neutralization assays, with the specific validated mutations in red.

Fig. 5:. Sera escape and ACE2 binding are inversely correlated for non-RBD and ACE2-distal RBD sites

a, Left: correlation between ACE2 binding and escape for the non-RBD sites with the highest mutation-level sera escape. Right: logo plot for the same sites, with letter heights proportional to escape (negative heights mean more neutralization), and letter colors indicating effect on ACE2 binding (green means better binding). b, A similar plot for RBD sites that are distal (at least 15 Å) from ACE2. c, A similar plot for RBD sites proximal to ACE2. Only sites with at least seven different mutations measured are included in the logo plots. d, Top-down view of XBB spike (PDB ID: 8IOT) with the non-RBD and ACE2-distal sites shown in panels a and b highlighted as spheres. The RBD is pink, the NTD is blue, and sites in SD1 are green.

Fig. 6:. Spike phenotypes measured by deep mutational scanning partially predict the evolutionary success of SARS-CoV-2 clades

a, Phylogenetic tree of XBB-descended Pango clades, colored by their relative growth rates. The tree shows only clades with at least 400 sequences and at least one new spike mutation, and their ancestors. Ancestor clades with insufficient sequences for growth rate estimates are in white. b, The same phylogeny but with branches colored by the change in growth rate between parent-descendant clade pairs. c, Correlation between the changes in growth rate for parent-descendant clade pairs versus the change in each spike phenotype measured in the XBB.1.5 full-spike deep mutational scanning (multiple mutations are assumed to have additive effects). The text above each plot shows the Pearson correlation (r) and a P-value computed by comparing the actual correlation to that for 100 randomizations of the experimental data among mutations. d, Ordinary least squares multiple linear regression of changes in growth rate versus all three measured spike phenotypes. The small text indicates the unique variance explained by each variable as well as the coefficients in the regression. See https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/current_dms_clade_pair_growth.html and https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/current_dms_ols_clade_pair_growth.html for interactive versions of panels c and d where points can be moused over for details on clades and their mutations.